Systems and methods for attenuation correction

ABSTRACT

A method include obtaining at least one first PET image of a subject acquired by a PET scanner and at least one first MR image of the subject acquired by an MR scanner. The method may also include obtaining a target neural network model. The target neural network model may provide a mapping relationship between PET images, MR images, and corresponding attenuation correction data, and output attenuation correction data associated with a specific PET image of the PET images. The method may further include generating first attenuation correction data corresponding to the subject using the target neural network model based on the at least one first PET image and the at least one first MR image of the subject, and determining a target PET image of the subject based on the first attenuation correction data corresponding to the subject.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a Continuation of International Application No.PCT/CN2018/111188, filed on Oct. 22, 2018, the contents of which isincorporated herein by reference.

TECHNICAL FIELD

The disclosure generally relates to imaging systems, and moreparticularly relates to systems and methods for attenuation correction.

BACKGROUND

Nuclear medicine imaging is widely used in the diagnosis and treatmentof various medical conditions based on images acquired by usingradiation emission. Positron emission tomography (PET) is an exemplarynuclear medicine imaging technique. PET is used to generate images thatmay reflect metabolic activities of a specific organ or tissue (e.g., atumor). Generally, a PET image may be reconstructed based on attenuationcorrection data to present accurate information of a specific organ ortissue in a body. With the development of multi-modality imagingtechnique (e.g., PET-magnetic resonance (MR) imaging technique), MRimages may be used for attenuation correction of a PET image. However,an MR image cannot directly present the electron density of tissue in abody, such that an attenuation correction image associated with the PETimage cannot be generated directly based on the MR image. It is desiredto provide systems and methods for PET image attenuation correctionbased on MR images.

SUMMARY

According to an aspect of the present disclosure, a system forattenuation correction is provided. The system may include at least onestorage device storing executable instructions, and at least oneprocessor in communication with the at least one storage device. Whenexecuting the executable instructions, the at least one processor maycause the system to obtain at least one first PET image of a subjectacquired by a PET scanner and at least one first MR image of the subjectacquired by an MR scanner. The at least one processor may also cause thesystem to obtain a target neural network model that provides a mappingrelationship between PET images, MR images, and correspondingattenuation correction data and outputs attenuation correction dataassociated with a specific PET image of the PET images. The at least oneprocessor may further cause the system to generate first attenuationcorrection data corresponding to the subject using the target neuralnetwork model based on the at least one first PET image and the at leastone first MR image of the subject. In some embodiments, the at least oneprocessor may also cause the system to determine a target PET image ofthe subject based on the first attenuation correction data correspondingto the subject.

In some embodiments, to obtain a target neural network model, the atleast one processor may be further configured to cause the system toobtain multiple groups of training data and generate the target neuralnetwork model by training a neural network model using the multiplegroups of training data. Each of the multiple groups of training datamay include a second PET image, a second MR image, and secondattenuation correction data corresponding to a sample.

In some embodiments, the neural network model may include aconvolutional neural network (CNN) model, a back propagation (BP) neuralnetwork model, a radial basis function (RBF) neural network model, adeep belief nets (DBN) neural network model, an Elman neural networkmodel, or the like, or a combination thereof.

In some embodiments, to obtain multiple groups of training data, the atleast one processor may be further configured to cause the system toobtain a CT image of the sample for each training data of a sample ofthe multiple groups of training data, and determine the secondattenuation correction data corresponding to the sample based on a CTimage of the sample.

In some embodiments, to obtain multiple groups of training data, the atleast one processor may be further configured to cause the system todetermine the second attenuation correction data corresponding to thesample based on the at least one of the second MR image or the secondPET image for each training data of a sample of the multiple groups oftraining data.

In some embodiments, to generate first attenuation correction datacorresponding to the subject using the target neural network model basedon the at least one first PET image and the at least one first MR imageof the subject, the at least one processor may be further configured tocause the system to input the at least one first PET image and the atleast one first MR image to the target neural network model, and obtainthe first attenuation correction data output by the target neuralnetwork model.

In some embodiments, to determine a target PET image of the subjectbased on the first attenuation correction data corresponding to thesubject, the at least one processor may be further configured to causethe system to obtain PET projection data associated with the first PETimage of the subject, and reconstruct the target PET image based on thePET projection data and the first attenuation correction data.

In some embodiments, to determine a target PET image of the subjectbased on the first attenuation correction data corresponding to thesubject, the at least one processor may be further configured to causethe system to perform a post-processing operation on the firstattenuation correction data corresponding to the subject. Thepost-processing operation may include an interpolation operation, aregistration operation, or the like, or a combination thereof.

In some embodiments, the at least one processor may be furtherconfigured to cause the system to perform a pre-processing operation onat least one of the at least one first PET image or the at least onefirst MR image. The pre-processing operation may include a filteringoperation, a smoothing operation, a transformation operation, adenoising operation, or the like, or a combination thereof.

According to another aspect of the present disclosure, a method forattenuation correction is provided. A method include obtaining at leastone first PET image of a subject acquired by a PET scanner and at leastone first MR image of the subject acquired by an MR scanner. The methodmay also include obtaining a target neural network model. The targetneural network model may provide a mapping relationship between PETimages, MR images, and corresponding attenuation correction data, andoutput attenuation correction data associated with a specific PET imageof the PET images. The method may further include generating firstattenuation correction data corresponding to the subject using thetarget neural network model based on the at least one first PET imageand the at least one first MR image of the subject, and determining atarget PET image of the subject based on the first attenuationcorrection data corresponding to the subject.

According to another aspect of the present disclosure, a non-transitorycomputer-readable medium storing at least one set of instructions may beprovided. When executed by at least one processor, the at least one setof instructions may direct the at least one processor to perform amethod. The method may include obtaining at least one first PET image ofa subject acquired by a PET scanner and at least one first MR image ofthe subject acquired by an MR scanner. The method may also includeobtaining a target neural network model. The target neural network modelmay provide a mapping relationship between PET images, MR images, andcorresponding attenuation correction data, and output attenuationcorrection data associated with a specific PET image of the PET images.The method may further include generating first attenuation correctiondata corresponding to the subject using the target neural network modelbased on the at least one first PET image and the at least one first MRimage of the subject, and determining a target PET image of the subjectbased on the first attenuation correction data corresponding to thesubject.

According to another aspect of the present disclosure, a system forattenuation correction may be provided. The system may include anacquisition module, a model determination module, and a correctionmodule. The acquisition module may be configured to obtain at least onefirst PET image of a subject acquired by a PET scanner and at least onefirst MR image of the subject acquired by an MR scanner. The modeldetermination module may be configured to obtain a target neural networkmodel. The target neural network model may provide a mappingrelationship between PET images, MR images, and correspondingattenuation correction data. The target neural network model may beconfigured to output attenuation correction data associated with aspecific PET image of the PET images. The correction module may beconfigured to generate first attenuation correction data correspondingto the subject using the target neural network model based on the atleast one first PET image and the at least one first MR image of thesubject, and determine a target PET image of the subject based on thefirst attenuation correction data corresponding to the subject.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. The drawings are not to scale. Theseembodiments are non-limiting exemplary embodiments, in which likereference numerals represent similar structures throughout the severalviews of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary imaging systemaccording to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary computing device on which the processingdevice 120 may be implemented according to some embodiments of thepresent disclosure;

FIG. 3 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary mobile device according to some embodimentsof the present disclosure;

FIG. 4 is a block diagram illustrating an exemplary processing deviceaccording to some embodiments of the present disclosure;

FIG. 5 is a flowchart illustrating an exemplary process for attenuationcorrection for PET imaging according to some embodiments of the presentdisclosure;

FIG. 6 is a flowchart illustrating an exemplary process for generating atarget neural network model for attenuation correction according to someembodiments of the present disclosure; and

FIG. 7 is a schematic diagram illustrating an exemplary back propagation(BP) neural network model according to some embodiments of the presentdisclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise,”“comprises,” and/or “comprising,” “include,” “includes,” and/or“including,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

It will be understood that the term “system,” “unit,” “module,” and/or“block” used herein are one method to distinguish different components,elements, parts, section or assembly of different level in ascendingorder. However, the terms may be displaced by another expression if theyachieve the same purpose.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or another storage device. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules/units/blocks configured for execution oncomputing devices (e.g., processing unit 320 as illustrated in FIG. 3)may be provided on a computer readable medium, such as a compact disc, adigital video disc, a flash drive, a magnetic disc, or any othertangible medium, or as a digital download (and can be originally storedin a compressed or installable format that needs installation,decompression, or decryption prior to execution). Such software code maybe stored, partially or fully, on a storage device of the executingcomputing device, for execution by the computing device. Softwareinstructions may be embedded in firmware, such as an EPROM. It will befurther appreciated that hardware modules/units/blocks may be includedof connected logic components, such as gates and flip-flops, and/or canbe included of programmable units, such as programmable gate arrays orprocessors. The modules/units/blocks or computing device functionalitydescribed herein may be implemented as software modules/units/blocks,but may be represented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to,” or “coupled to,” anotherunit, engine, module, or block, it may be directly on, connected orcoupled to, or communicate with the other unit, engine, module, orblock, or an intervening unit, engine, module, or block may be present,unless the context clearly indicates otherwise. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

Provided herein are systems and components for attenuation correction. Asystem for attenuation correction may interact with a PET scanner and anMR scanner to obtain and/or retrieve from a storage device PET imagedata and MR image data, respectively. The system may include at leastone storage device storing executable instructions, and at least oneprocessor in communication with the at least one storage device. Whenexecuting the executable instructions, the at least one processor maycause the system to obtain at least one first PET image of a subject(e.g., acquired by the PET scanner, retrieved from a storage device) andat least one first MR image of the subject (e.g., acquired by the MRscanner, retrieved from a storage device). The at least one processormay also cause the system to obtain a target neural network model thatprovides a mapping relationship between PET images, MR images, andcorresponding attenuation correction data and outputs attenuationcorrection data associated with a specific PET image data. The at leastone processor may further cause the system to generate the firstattenuation correction data corresponding to the subject by inputtingthe at least one first PET image and the at least one first MR image ofthe subject in the target neural network model, and determine a targetPET image of the subject based on the first attenuation correction datacorresponding to the subject.

Accordingly, the system may generate attenuation correction dataassociated with a PET image directly by inputting the PET image and anMR image of a subject into the target neural network model, which mayimprove processing speed for generating the attenuation correction dataand may be applied in different clinical situations. In someembodiments, the system may update a plurality of training samplesaccording to clinical demands and update the target neural network modelby training the target neural network model using the updated pluralityof training samples. Accordingly, the system may adapt to complexclinical situations and have improved robustness.

FIG. 1 is a schematic diagram illustrating an exemplary imaging system100 according to some embodiments of the present disclosure. In someembodiments, the imaging system 100 may be a single-modality system or amulti-modality system. Exemplary single-modality systems may include apositron emission tomography (PET) system, a magnetic resonance (MR)system, etc. Exemplary multi-modality systems may include a computedtomography-positron emission tomography (CT-PET) system, a magneticresonance-positron emission tomography (MR-PET) system, etc. In someembodiments, the imaging system 100 may include modules and/orcomponents for performing imaging and/or related analysis.

Merely by way of example, as illustrated in FIG. 1, the imaging system100 may include a medical device 110, a processing device 120, a storagedevice 130, one or more terminals 140, and a network 150. The componentsin the imaging system 100 may be connected in one or more of variousways. Merely by way of example, the medical device 110 may be connectedto the processing device 120 through the network 150. As anotherexample, the medical device 110 may be connected to the processingdevice 120 directly as illustrated in FIG. 1. As a further example, theterminal(s) 140 may be connected to another component of the imagingsystem 100 (e.g., the processing device 120) via the network 150. Asstill a further example, the terminal(s) 140 may be connected to theprocessing device 120 directly as illustrated by the dotted arrow inFIG. 1. As still a further example, the storage device 130 may beconnected to another component of the imaging system 100 (e.g., theprocessing device 120) directly as illustrated in FIG. 1, or through thenetwork 150.

The medical device 110 may include a multi-modality imaging device. Themulti-modality imaging device may acquire imaging data relating to atleast one part of a subject. The imaging data relating to at least onepart of a subject may include an image (e.g., an image slice),projection data, or a combination thereof. In some embodiments, theimaging data may be a two-dimensional (2D) imaging data, athree-dimensional (3D) imaging data, a four-dimensional (4D) imagingdata, or the like, or any combination thereof. The subject may bebiological or non-biological. For example, the subject may include apatient, a man-made object, etc. As another example, the subject mayinclude a specific portion, organ, and/or tissue of the patient. Forexample, the subject may include the head, the neck, the thorax, theheart, the stomach, a blood vessel, soft tissue, a tumor, nodules, orthe like, or any combination thereof. Exemplary multi-modality imagingdevices may include a PET-CT scanner, a PET-MR scanner, or the like, ora combination thereof. For example, the medical device 110 may include aPET scanner and an MR scanner. The PET scanner may scan a subject or aportion thereof that is located within its detection region and generateprojection data relating to the subject or the portion thereof. The PETmay include a gantry, a detector, an electronics module, and/or othercomponents not shown. The gantry may support one or more parts of thePET scanner, for example, the detector, the electronics module, and/orother components. The detector may detect radiation photons (e.g., γphotons) emitted from a subject being examined. The electronics modulemay collect and/or process electrical signals (e.g., scintillationpulses) generated by the detector. The electronics module may convert ananalog signal (e.g., an electrical signal generated by the detector)relating to a radiation photon detected by the detector to a digitalsignal relating to a radiation event. As used herein, a radiation event(also referred to as a single event) may refer to an interaction betweena radiation photon emitted from a subject and impinging on and detectedby the detector. A pair of radiation photons (e.g., γ photons)interacting with two detector blocks along a line of response (LOR)within a coincidence time window may be determined as a coincidenceevent. A portion of the radiation photons (e.g., γ photons) emitted froma subject being examined may interact with tissue in the subject. Theradiation photons (e.g., γ photons) interacting with tissue in thesubject may be scattered or otherwise change its trajectory, that mayaffect the number or count of radiation photons (e.g., γ photons)detected by two detector blocks along a line of response (LOR) within acoincidence time window and the number or count of coincidence events.

The MR scanner may scan a subject or a portion thereof that is locatedwithin its detection region and generate MR image data relating to thesubject or the portion thereof. The MR image data may include k-spacedata, MR signals, an MR image, etc. The MR image data may be acquired bythe MR scanner via scanning the subject using a pulse sequence.Exemplary pulse sequences may include a spin echo sequence, a gradientecho sequence, a diffusion sequence, an inversion recovery sequence, orthe like, or a combination thereof. For example, the spin echo sequencemay include a fast spin echo (FSE), a turbo spin echo (TSE), a rapidacquisition with relaxation enhancement (RARE), a half-Fourieracquisition single-shot turbo spin-echo (HASTE), a turbo gradient spinecho (TGSE), or the like, or a combination thereof.

The processing device 120 may process data and/or information obtainedfrom the medical device 110, the terminal(s) 140, and/or the storagedevice 130. For example, the processing device 120 may obtain a PETimage and an MR image relating to a subject. The processing device 120may also obtain a target neural network model for attenuationcorrection. The target neural network model may provide a mappingrelationship between PET images, MR images, and correspondingattenuation correction data, and output attenuation correction dataassociated with a specific PET image. The processing device 120 maydetermine the attenuation correction associated with the PET image basedon the mapping relationship using the target neural network model. Asanother example, the processing device 120 may reconstruct a target PETimage of the subject based on the attenuation correction data associatedwith the PET image. As still another example, the processing device 120may obtain a plurality of training data. The processing device 120 maygenerate the target neural network model by training a neural networkmodel using the plurality of training data.

In some embodiments, the processing device 120 may be a computer, a userconsole, a single server or a server group, etc. The server group may becentralized or distributed. In some embodiments, the processing device120 may be local or remote. For example, the processing device 120 mayaccess information and/or data stored in the medical device 110, theterminal(s) 140, and/or the storage device 130 via the network 150. Asanother example, the processing device 120 may be directly connected tothe medical device 110, the terminal(s) 140 and/or the storage device130 to access stored information and/or data. In some embodiments, theprocessing device 120 may be implemented on a cloud platform. Merely byway of example, the cloud platform may include a private cloud, a publiccloud, a hybrid cloud, a community cloud, a distributed cloud, aninter-cloud, a multi-cloud, or the like, or any combination thereof.

The storage device 130 may store data, instructions, and/or any otherinformation. In some embodiments, the storage device 130 may store dataobtained from the terminal(s) 140 and/or the processing device 120. Thedata may include image data acquired by the processing device 120,algorithms and/or models for processing the image data, etc. Forexample, the storage device 130 may store image data (e.g., PET images,MR images, PET projection data, etc.) acquired by the medical device110. As another example, the storage device 130 may store one or morealgorithms for processing the image data, a target neural network modelfor generating attenuation data, etc. In some embodiments, the storagedevice 130 may store data and/or instructions that the processing device120 may execute or use to perform exemplary methods/systems described inthe present disclosure. In some embodiments, the storage device 130 mayinclude mass storage, removable storage, volatile read-and-write memory,read-only memory (ROM), or the like, or any combination thereof.Exemplary mass storage may include a magnetic disk, an optical disk, asolid-state drive, etc. Exemplary removable storage may include a flashdrive, a floppy disk, an optical disk, a memory card, a zip disk, amagnetic tape, etc. Exemplary volatile read-and-write memories mayinclude a random access memory (RAM). Exemplary RAM may include adynamic RAM (DRAM), a double date rate synchronous dynamic RAM (DDRSDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and azero-capacitor RAM (Z-RAM), etc. Exemplary ROM may include a mask ROM(MROM), a programmable ROM (PROM), an erasable programmable ROM (EPROM),an electrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), and a digital versatile disk ROM, etc. In some embodiments,the storage device 130 may be implemented on a cloud platform. Merely byway of example, the cloud platform may include a private cloud, a publiccloud, a hybrid cloud, a community cloud, a distributed cloud, aninter-cloud, a multi-cloud, or the like, or any combination thereof.

In some embodiments, the storage device 130 may be connected to thenetwork 150 to communicate with one or more other components in theimaging system 100 (e.g., the processing device 120, the terminal(s)140, etc.). One or more components in the imaging system 100 may accessthe data or instructions stored in the storage device 130 via thenetwork 150. In some embodiments, the storage device 130 may be directlyconnected to or communicate with one or more other components in theimaging system 100 (e.g., the processing device 120, the terminal(s)140, etc.). In some embodiments, the storage device 130 may be part ofthe processing device 120.

The terminal(s) 140 may include a mobile device 140-1, a tablet computer140-2, a laptop computer 140-3, or the like, or any combination thereof.In some embodiments, the mobile device 140-1 may include a smart homedevice, a wearable device, a mobile device, a virtual reality device, anaugmented reality device, or the like, or any combination thereof. Insome embodiments, the smart home device may include a smart lightingdevice, a control device of an intelligent electrical apparatus, a smartmonitoring device, a smart television, a smart video camera, aninterphone, or the like, or any combination thereof. In someembodiments, the wearable device may include a bracelet, a footgear,eyeglasses, a helmet, a watch, clothing, a backpack, a smart accessory,or the like, or any combination thereof. In some embodiments, the mobiledevice may include a mobile phone, a personal digital assistant (PDA), agaming device, a navigation device, a point of sale (POS) device, alaptop, a tablet computer, a desktop, or the like, or any combinationthereof. In some embodiments, the virtual reality device and/or theaugmented reality device may include a virtual reality helmet, virtualreality glasses, a virtual reality patch, an augmented reality helmet,augmented reality glasses, an augmented reality patch, or the like, orany combination thereof. For example, the virtual reality device and/orthe augmented reality device may include a Google Glass™, an OculusRift™, a Hololens™ a Gear VR™, etc. In some embodiments, the terminal(s)140 may be part of the processing device 120.

The network 150 may include any suitable network that can facilitate theexchange of information and/or data for the imaging system 100. In someembodiments, one or more components of the medical device 110 (e.g., aCT scanner, a PET scanner, etc.), the terminal(s) 140, the processingdevice 120, the storage device 130, etc., may communicate informationand/or data with one or more other components of the imaging system 100via the network 150. For example, the processing device 120 may obtaindata from the medical device 110 via the network 150. As anotherexample, the processing device 120 may obtain user instructions from theterminal(s) 140 via the network 150. The network 150 may be and/orinclude a public network (e.g., the Internet), a private network (e.g.,a local area network (LAN), a wide area network (WAN)), etc.), a wirednetwork (e.g., an Ethernet network), a wireless network (e.g., an 802.11network, a Wi-Fi network, etc.), a cellular network (e.g., a Long TermEvolution (LTE) network), a frame relay network, a virtual privatenetwork (“VPN”), a satellite network, a telephone network, routers,hubs, switches, server computers, and/or any combination thereof. Merelyby way of example, the network 150 may include a cable network, awireline network, a fiber-optic network, a telecommunications network,an intranet, a wireless local area network (WLAN), a metropolitan areanetwork (MAN), a public telephone switched network (PSTN), a Bluetooth™network, a ZigBee™ network, a near field communication (NFC) network, orthe like, or any combination thereof. In some embodiments, the network150 may include one or more network access points. For example, thenetwork 150 may include wired and/or wireless network access points suchas base stations and/or internet exchange points through which one ormore components of the imaging system 100 may be connected to thenetwork 150 to exchange data and/or information.

It should be noted that the above description of the imaging system 100is merely provided for the purposes of illustration, and not intended tolimit the scope of the present disclosure. For persons having ordinaryskills in the art, multiple variations and modifications may be madeunder the teachings of the present disclosure. For example, the assemblyand/or function of the imaging system 100 may be varied or changedaccording to specific implementation scenarios.

FIG. 2 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary computing device 200 on which the processingdevice 120 may be implemented according to some embodiments of thepresent disclosure. As illustrated in FIG. 2, the computing device 200may include a processor 210, a storage 220, an input/output (I/O) 230,and a communication port 240.

The processor 210 may execute computer instructions (program code) andperform functions of the processing device 120 in accordance withtechniques described herein. The computer instructions may include, forexample, routines, programs, objects, components, signals, datastructures, procedures, modules, and functions, which perform particularfunctions described herein. For example, the processor 210 may processdata obtained from the medical device 110, the terminal(s) 140, thestorage device 130, and/or any other component of the imaging system100. Specifically, the processor 210 may process one or more measureddata sets obtained from the medical device 110. For example, theprocessor 210 may reconstruct an image based on the data set(s). In someembodiments, the reconstructed image may be stored in the storage device130, the storage 220, etc. In some embodiments, the reconstructed imagemay be displayed on a display device by the I/O 230. In someembodiments, the processor 210 may perform instructions obtained fromthe terminal(s) 140. In some embodiments, the processor 210 may includeone or more hardware processors, such as a microcontroller, amicroprocessor, a reduced instruction set computer (RISC), anapplication specific integrated circuits (ASICs), anapplication-specific instruction-set processor (ASIP), a centralprocessing unit (CPU), a graphics processing unit (GPU), a physicsprocessing unit (PPU), a microcontroller unit, a digital signalprocessor (DSP), a field programmable gate array (FPGA), an advancedRISC machine (ARM), a programmable logic device (PLD), any circuit orprocessor capable of executing one or more functions, or the like, orany combinations thereof.

Merely for illustration, only one processor is described in thecomputing device 200. However, it should be noted that the computingdevice 200 in the present disclosure may also include multipleprocessors. Thus operations and/or method steps that are performed byone processor as described in the present disclosure may also be jointlyor separately performed by the multiple processors. For example, if inthe present disclosure the processor of the computing device 200executes both operation A and operation B, it should be understood thatoperation A and operation B may also be performed by two or moredifferent processors jointly or separately in the computing device 200(e.g., a first processor executes operation A and a second processorexecutes operation B, or the first and second processors jointly executeoperations A and B).

The storage 220 may store data/information obtained from the medicaldevice 110, the terminal(s) 140, the storage device 130, or any othercomponent of the imaging system 100. In some embodiments, the storage220 may include a mass storage device, a removable storage device, avolatile read-and-write memory, a read-only memory (ROM), or the like,or any combination thereof. For example, the mass storage may include amagnetic disk, an optical disk, a solid-state drive, etc. The removablestorage may include a flash drive, a floppy disk, an optical disk, amemory card, a zip disk, a magnetic tape, etc. The volatileread-and-write memory may include a random access memory (RAM). The RAMmay include a dynamic RAM (DRAM), a double date rate synchronous dynamicRAM (DDR SDRAM), a static RAM (SRAM), a thyristor RAM (T-RAM), and azero-capacitor RAM (Z-RAM), etc. The ROM may include a mask ROM (MROM),a programmable ROM (PROM), an erasable programmable ROM (PEROM), anelectrically erasable programmable ROM (EEPROM), a compact disk ROM(CD-ROM), and a digital versatile disk ROM, etc. In some embodiments,the storage 220 may store one or more programs and/or instructions toperform exemplary methods described in the present disclosure. Forexample, the storage 220 may store a program for the processing device120 for generating attenuation correction data for a PET image.

The I/O 230 may input or output signals, data, and/or information. Insome embodiments, the I/O 230 may enable user interaction with theprocessing device 120. In some embodiments, the I/O 230 may include aninput device and an output device. Exemplary input devices may include akeyboard, a mouse, a touch screen, a microphone, or the like, or acombination thereof. Exemplary output devices may include a displaydevice, a loudspeaker, a printer, a projector, or the like, or acombination thereof. Exemplary display devices may include a liquidcrystal display (LCD), a light-emitting diode (LED)-based display, aflat panel display, a curved screen, a television device, a cathode raytube (CRT), or the like, or a combination thereof.

The communication port 240 may be connected with a network (e.g., thenetwork 150) to facilitate data communications. The communication port240 may establish connections between the processing device 120 and themedical device 110, the terminal(s) 140, or the storage device 130. Theconnection may be a wired connection, a wireless connection, or acombination of both that enables data transmission and reception. Thewired connection may include an electrical cable, an optical cable, atelephone wire, or the like, or any combination thereof. The wirelessconnection may include Bluetooth, Wi-Fi, WiMax, WLAN, ZigBee, mobilenetwork (e.g., 3G, 4G, 5G, etc.), or the like, or a combination thereof.In some embodiments, the communication port 240 may be a standardizedcommunication port, such as RS232, RS485, etc. In some embodiments, thecommunication port 240 may be a specially designed communication port.For example, the communication port 240 may be designed in accordancewith the digital imaging and communications in medicine (DICOM)protocol.

FIG. 3 is a schematic diagram illustrating hardware and/or softwarecomponents of an exemplary mobile device 300 according to someembodiments of the present disclosure. As illustrated in FIG. 3, themobile device 300 may include a communication platform 310, a display320, a graphics processing unit (GPU) 330, a central processing unit(CPU) 340, an I/O 350, a memory 360, and a storage 390. In someembodiments, any other suitable component, including but not limited toa system bus or a controller (not shown), may also be included in themobile device 300. In some embodiments, a mobile operating system 370(e.g., iOS, Android, Windows Phone, etc.) and one or more applications380 may be loaded into the memory 360 from the storage 390 in order tobe executed by the CPU 340. The applications 380 may include a browseror any other suitable mobile apps for receiving and renderinginformation relating to image processing or other information from theprocessing device 120. User interactions with the information stream maybe achieved via the I/O 350 and provided to the processing device 120and/or other components of the imaging system 100 via the network 120.

To implement various modules, units, and their functionalities describedin the present disclosure, computer hardware platforms may be used asthe hardware platform(s) for one or more of the elements describedherein. The hardware elements, operating systems and programminglanguages of such computers are conventional in nature, and it ispresumed that those skilled in the art are adequately familiar therewithto adapt those technologies to generate an image as described herein. Acomputer with user interface elements may be used to implement apersonal computer (PC) or another type of work station or terminaldevice, although a computer may also act as a server if appropriatelyprogrammed. It is believed that those skilled in the art are familiarwith the structure, programming and general operation of such computerequipment and as a result, the drawings should be self-explanatory.

FIG. 4 is a block diagram illustrating an exemplary processing device120 according to some embodiments of the present disclosure. In someembodiments, the processing device 120 may be implemented on a computingdevice as illustrated in FIG. 2 or a mobile device as illustrated inFIG. 3. As illustrated in FIG. 4, the processing device 120 may includean acquisition module 402, a pre-processing module 404, a modeldetermination module 406, a post-processing module 408, a correctionmodule 410, and a storage module 412. In some embodiments, the modulesmay be connected with each other via a wired connection (e.g., a metalcable, an optical cable, a hybrid cable, or the like, or any combinationthereof) or a wireless connection (e.g., a Local Area Network (LAN), aWide Area Network (WAN), a Bluetooth, a ZigBee, a Near FieldCommunication (NFC), or the like, or a combination thereof).

The acquisition module 402 may be configured to obtain informationand/or data for attenuation correction. In some embodiments, theacquisition module 402 may be configured to obtain one or more PETimages and one or more MR images relating to one or more subjects. Insome embodiments, the acquisition module 402 may be configured to obtaina target neural network model that provides a mapping relationshipbetween PET images, MR images, and corresponding attenuation correctiondata, and output attenuation correction data associated with a specificPET image. The acquisition module 402 may transmit the informationand/or data for attenuation correction to other components of theprocessing device 120 for further processing. For example, theacquisition module 402 may transmit a PET image, an MR image, and atarget neural network model to the correction module 410 to determineattenuation correction data associated with the PET image.

The pre-processing module 404 may be configured to perform one or morepre-processing operations on an image. The image may include a PETimage, an MR image, or the like, or a combination thereof. Thepre-processing operation may be performed to adjust the quality of animage (e.g., a PET image, an MR image, etc.), such as the noise level ofan image, the contrast ratio of an image, etc. In some embodiments, thepre-processing operation may include a denoising operation, anenhancement operation, a smoothing operation, a fusion operation, asegmentation operation, a registration operation, a transformationoperation, or the like, or a combination thereof. The pre-processingmodule 404 may transmit the pre-processed image to other components ofthe processing device 120 for further processing. For example, thepre-processing module 404 may transmit a pre-processed PET image and/orpre-processed MR image to the correction module 410 to determineattenuation correction data associated with the PET image.

The model determination module 406 may be configured to generate atarget neural network model for attenuation correction. The targetneural network model may provide a mapping relationship between PETimages, MR images, and corresponding attenuation correction data. Thetarget neural network model may be configured to output attenuationcorrection data associated with a specific PET image when the specificPET image and a corresponding MR image are inputted into the targetneural network model based on the mapping relationship. The targetneural network model may be constructed based on a neural network model.Exemplary neural network models may include a back propagation (BP)neural network model, a radial basis function (RBF) neural networkmodel, a deep belief nets (DBN) neural network model, an Elman neuralnetwork model, or the like, or a combination thereof. In someembodiments, the model determination module 406 may generate the targetneural network model by training the neural network model using aplurality of groups of training data relating to multiple samples. Eachof the plurality of groups of training data may include a PET image, anMR image, and attenuation correction data corresponding to a sample. Themodel determination module 406 may transmit the target neural networkmodel to other components of the processing device 120 for furtherprocessing. For example, the model determination module 406 may transmitthe target neural network model to the correction module 410 todetermine attenuation correction data associated with a PET image.

The post-processing module 408 may be configured to perform apost-processing on attenuation correction data associated with a PETimage. In some embodiments, the post-processing operation may include aninterpolation operation, a registration operation, a transformationoperation, or the like, or a combination thereof. The interpolationoperation may be performed using, for example, a nearest neighborinterpolation algorithm, a bilinear interpolation algorithm, a doublesquare interpolation algorithm, a bicubic interpolation algorithm, orthe like, or a combination thereof. The registration operation may beperformed using, for example, a cross-correlation algorithm, a Walshtransform algorithm, a phase correlation algorithm, etc. Thepost-processing module 408 may transmit the post-processed attenuationcorrection data to other components of the processing device 120 forfurther processing. For example, the post-processing module 408 maytransmit the post-processed attenuation correction data to thecorrection module 410 to correct a PET image.

The correction module 410 may be configured to generate attenuationcorrection data associated with a PET image, correct the PET image,and/or generate a target PET image relating to a subject based on theattenuation correction data associated with the PET image. In someembodiments, the attenuation correction data associated with the PETimage may be generated by inputting the PET image and a corresponding MRimage into the target neural network model. The target neural networkmodel may determine the attenuation correction data associated with thePET image based on the mapping relationship. Then the target neuralnetwork model may be configured to output the attenuation correctiondata associated with the PET image.

In some embodiments, the target PET image may be reconstructed based onPET projection data (e.g., sonogram data) and the attenuation correctiondata associated with the PET image using a PET image reconstructiontechnique as described elsewhere in the present disclosure. In someembodiments, the target PET image may be generated by correcting the PETimage based on the attenuation correction data associated with the PETimage. The correction module 410 may transmit the target PET imagerelating to a subject to other components of the imaging system 100. Forexample, the correction module 410 may transmit the target PET image tothe terminal(s) 140 for display or the storage device 130 for store.

The storage module 412 may store information. The information mayinclude programs, software, algorithms, data, text, number, images andsome other information. For example, the information may include a PETimage, an MR image, attenuation correction data associated with the PETimage, etc.

It should be noted that the above description of the processing device120 is merely provided for the purposes of illustration, and notintended to limit the scope of the present disclosure. For personshaving ordinary skills in the art, multiple variations and modificationsmay be made under the teachings of the present disclosure. For instance,the assembly and/or function of the processing device 120 may be variedor changed according to specific implementation scenarios. Merely by wayof example, the pre-processing module 404 and the post-processing module408 may be integrated into a single module. As another example, someother components/modules may be added into the processing device 120.Such variations and modifications do not depart from the scope of thepresent disclosure.

FIG. 5 is a flowchart illustrating an exemplary process 500 ofattenuation correction for PET imaging according to some embodiments ofthe present disclosure. In some embodiments, one or more operations ofthe process 500 illustrated in FIG. 5 may be implemented in the imagingsystem 100 illustrated in FIG. 1. For example, process 500 illustratedin FIG. 5 may be stored in the storage device 130 in the form ofinstructions, and invoked and/or executed by the processing device 120(e.g., the processor 210 of the computing device 200 as illustrated inFIG. 2, the GPU 330 or CPU 340 of the mobile device 300 as illustratedin FIG. 3).

In 502, at least one PET image of a subject may be obtained. Operation502 may be performed by the acquisition module 402. In some embodiments,the at least one PET image (also referred to as at least one first PETimage) may be acquired by a PET scanner, such as a PET scanner of amulti-modality imaging device (e.g., a PET scanner of the medical device110). The acquisition module 402 may obtain the at least one PET imageof the subject from the PET scanner, the storage device 130, the storage220, the storage 390, or any other storage device. In some embodiments,the at least one PET image of the subject may be reconstructed based onPET projection data of the subject using a PET image reconstructiontechnique. The PET projection data (e.g., sonogram data) may be acquiredby the PET scanner via scanning the subject. Exemplary PET imagereconstruction techniques may include an iterative reconstructionalgorithm, a Fourier slice theorem algorithm, a filtered back projection(FBP) algorithm, a fan-beam reconstruction algorithm, an analyticreconstruction algorithm, or the like, or any combination thereof.Exemplary iterative reconstruction algorithms may include a statisticalreconstruction algorithm, a maximum likelihood expectation maximumalgorithm, a conjugate gradient (CG) algorithm, a maximum a posteriori(MAP) algorithm, etc. The at least one PET image may include a twodimensional (2D) PET image, a three dimensional (3D) PET image, a fourthdimensional (4D) PET image, etc.

In 504, at least one MR image of the subject may be obtained. Operation504 may be performed by the acquisition module 402. In some embodiments,the at least one MR image (also referred to as at least one first MRimage) may be acquired by an MR scanner, such as an MR scanner of themulti-modality imaging device (e.g., an MR scanner of the medical device110). The acquisition module 402 may obtain the at least one MR image ofthe subject from the MR scanner, the storage device 130, the storage220, the storage 390, or any other storage device. In some embodiments,the at least one MR image of the subject may be reconstructed based onscan data relating to the subject using an MR image reconstructiontechnique. Exemplary MR image reconstruction techniques may include a2-dimensional Fourier transform technique, a back projection technique(e.g., a convolution back projection technique, a filtered backprojection technique), an iteration technique, etc. Exemplary iterationtechniques may include an algebraic reconstruction technique (ART), asimultaneous iterative reconstruction technique (SIRT), a simultaneousalgebraic reconstruction technique (SART), an adaptive statisticaliterative reconstruction (ASIR) technique, a model-based iterativereconstruction (MBIR) technique, a sinogram affirmed iterativereconstruction (SAFIR) technique, or the like, or any combinationthereof. The at least one MR image reconstructed based on the scan datamay include a gradient echo MR image, a spin echo MR image, or the like,or a combination thereof. The at least one MR image may include a twodimensional (2D) MR image, a three dimensional (3D) MR image, a fourthdimensional (4D) MR image, etc.

In 506, the at least one PET image and/or the at least one MR image maybe pre-processed. Operation 506 may be performed by the pre-processingmodule 404. The pre-processing operation may be performed to adjust thequality of an image (e.g., the at least one PET image, the at least oneMR image, etc.), such as the noise level of an image, the contrast of animage, the artifact of an image, the resolution of an image, etc. Insome embodiments, the pre-processing operation may include a denoisingoperation, an enhancement operation, a smoothing operation, a fusionoperation, a segmentation operation, a registration operation, atransformation operation, or the like, or a combination thereof.Specifically, the smoothing operation may be performed based on aGaussian filter, an average filter, a median filter, a wavelettransformation, or the like, or a combination thereof. The enhancementoperation may include a histogram equalization, an image sharpening, aFourier transform, a high-pass filtering, a low-pass filtering, or thelike, or a combination thereof. The denoising operation may includeapplying a spatial-domain filter, a transform-domain filter, amorphological noise filter, or the like, or a combination thereof. Thesegmentation operation may be performed based on a segmentationalgorithm. Exemplary segmentation algorithms may include athreshold-based segmentation algorithm, an edge-based segmentationalgorithm, a region-based segmentation algorithm, or the like, or acombination thereof. The fusion operation may be performed using, forexample, an optimal seam-line algorithm, a gradient pyramid algorithm,etc. The registration operation may be performed using, for example, across-correlation algorithm, a Walsh transform algorithm, a phasecorrelation algorithm, etc. The transformation operation may include animage geometric transformation, an image perspective transformation, animage affine transformation, etc.

In 508, a target neural network model that provides a mappingrelationship between PET images, MR images, and correspondingattenuation correction data may be obtained. Operation 508 may beperformed by the acquisition module 402 and/or the model determinationmodule 406. The target neural network model may be configured to outputattenuation correction data associated with a specific PET image whenthe specific PET image and a corresponding MR image are inputted intothe target neural network model based on the mapping relationship. Insome embodiments, the acquisition module 402 may obtain the targetneural network model from the storage device 130, the storage 220, thestorage 390, the storage module 412, or any other storage device. Insome embodiments, the model determination module 406 may generate thetarget neural network model by training a neural network model using aplurality of groups of training data relating to multiple samples.Exemplary neural network models may include a back propagation (BP)neural network model, a radial basis function (RBF) neural networkmodel, a deep belief nets (DBN) neural network model, an Elman neuralnetwork model, or the like, or a combination thereof. Each of theplurality of groups of training data may include a PET image (alsoreferred to as a second PET image), an MR image (also referred to as asecond MR image), and reference attenuation correction data (alsoreferred to as second attenuation correction data) corresponding to asample. During a training process of the neural network model, themapping relationship between a PET image, an MR image, and attenuationcorrection data associated with the PET image may be established basedon the plurality of groups of training samples, and the trained neuralnetwork model may be determined as the target neural network model. Moredescriptions for generating a target neural network model may be foundelsewhere in the present disclosure (e.g., FIG. 6 and FIG. 7, and thedescriptions thereof).

In 510, target attenuation correction data associated with the at leastone PET image may be generated based on the at least one PET image andthe at least one MR image using the target neural network model.Operation 510 may be performed by the correction module 410. In someembodiments, the target attenuation correction data (also referred to asfirst attenuation correction data) associated with the at least one PETimage may be generated by inputting the at least one PET image (the atleast one PET image obtained in 502 or the at least one pre-processedPET image in 506) and the at least one MR image (the at least one MRimage obtained in 504 or the at least one pre-processed MR image in 506)into the target neural network model. The target neural network modelmay determine the target attenuation correction data associated with theat least one PET image based on the mapping relationship. Then thetarget neural network model may be configured to output the targetattenuation correction data associated with the at least one PET image.

The target attenuation correction data associated with the at least onePET image may present the distribution of attenuation coefficientsrelating to various portions or compositions of the subject. The targetattenuation correction data may be in form of an image, a matrix, amask, etc. In some embodiments, the target attenuation correction dataassociated with the at least one PET image may include an attenuationcorrection image corresponding to the subject. The attenuationcorrection image corresponding to the subject may include a 2Dattenuation correction image, a 3D attenuation correction image, etc.The attenuation correction image corresponding to the subject maypresent the subject based on a plurality of pixels or voxels. Theattenuation coefficients relating to various portions or compositions ofthe subject may be denoted by the values of the plurality of pixels orvoxels in the attenuation correction image. In some embodiments, thetarget attenuation correction data associated with the at least one PETimage may be denoted by a matrix (e.g., a 2D matrix, a 3D matrix, etc.)including a plurality of elements. One of the plurality of elements maydenote an attenuation coefficient associated with at least one portionof the subject.

In 512, the target attenuation correction data associated with the atleast one PET image may be post-processed. Operation 512 may beperformed by the post-processing module 408. In some embodiments, thepost-processing operation may include an interpolation operation, aregistration operation, a transformation operation, or the like, or acombination thereof. The interpolation operation may be performed using,for example, a nearest neighbor interpolation algorithm, a bilinearinterpolation algorithm, a double square interpolation algorithm, abicubic interpolation algorithm, or the like, or a combination thereof.The interpolation operation performed on the target attenuationcorrection data may adjust the resolution of the target attenuationcorrection data (e.g., an attenuation correction image) associated withthe at least one PET image. For example, the at least one PET image maybe expressed in the form of a first matrix including a plurality offirst elements. The target attenuation correction data associated withthe at least one PET image may be expressed in the form of a secondmatrix including a plurality of second elements. One of the plurality ofsecond elements may correspond to one or more of the plurality of firstelements. The interpolation operation performed on the targetattenuation correction data may cause each of the plurality of secondelements of the target attenuation correction data to correspond to onesingle first element in the at least one PET image.

The registration operation may be performed using, for example, across-correlation algorithm, a Walsh transform algorithm, a phasecorrelation algorithm, etc. The registration operation may be performedbetween the at least one PET image and the target attenuation correctiondata to match an element of the at least one PET image with acorresponding element of the target attenuation correction data. As usedherein, an element of the at least one PET image may be consideredcorresponding to an element of the target attenuation correction data ifthe element of the at least one PET image and the element of theattenuation correction data correspond to a same spatial position orportion of the subject. For example, the registration operation may beperformed to match an element of the attenuation correction data with acorresponding element of the at least one PET image. The element of theattenuation correction data and the corresponding element of theattenuation correction data may correspond to a same spatial position orportion of the subject.

In 514, a target PET image relating to the subject may be determinedbased on the target attenuation correction data associated with the atleast one PET image. Operation 514 may be performed by the correctionmodule 410. In some embodiments, the target PET image may bereconstructed based on PET projection data (e.g., sonogram data)associated with the at least one PET image and the target attenuationcorrection data (the attenuation correction data generated in 510 or thepost-processed attenuation correction data in 512) using a PET imagereconstruction technique as described elsewhere in the presentdisclosure. For example, the target attenuation correction data (e.g.,an attenuation correction image) may be projected to generate projectedattenuation data using a projection transformation technique (e.g., aRadon transform). The target PET image may be reconstructed based on theprojected attenuation data and the PET projection data (e.g., sonogramdata) relating to the subject using a PET image reconstruction techniqueas described elsewhere in the present disclosure. The PET projectiondata associated with the at least one PET image may be obtained from thePET scanner, the storage device 130, the storage 220, the storage 390,or any other storage device.

In some embodiments, the target PET image may be generated by correctingthe at least one PET image based on the target attenuation correctiondata associated with the at least one PET image. For example, the atleast one PET image may be expressed in the form of a first matrixincluding a plurality of first elements. The target attenuationcorrection data associated with the at least one PET image may beexpressed in the form of a second matrix including a plurality of secondelements. One of the plurality of second elements may correspond to oneor more of the plurality of first elements. The target PET image may begenerated by multiplying each of the plurality of first elements with acorresponding second element.

Accordingly, the system may generate attenuation correction dataassociated with a PET image directly by inputting the PET image and anMR image of a subject into the target neural network model, which mayimprove processing speed for generating the attenuation correction dataand may be applied in different clinical situations.

It should be noted that the above description of the process ofattenuation correction for PET imaging is merely provided for thepurposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. For example, operation 502 and operation 504 maybe performed simultaneously. As another example, process 500 may furtherinclude storing the at least one PET image, the at least one MR image,and the target attenuation data associated with the at least one PETimage. The at least one PET image, the at least one MR image, and thetarget attenuation data associated with the at least one PET image maybe used to update a training set of the target neural network model.Process 500 may further include updating the target neural network modelbased on the updated training set. As still a further example, operation506 and/or operation 512 may be omitted. Such variations andmodifications do not depart from the scope of the present disclosure.

FIG. 6 is a flowchart illustrating an exemplary process 600 ofgenerating a target neural network model for attenuation correctionaccording to some embodiments of the present disclosure. In someembodiments, one or more operations of the process 600 illustrated inFIG. 6 may be implemented in the imaging system 100 illustrated inFIG. 1. For example, the process 600 illustrated in FIG. 6 may be storedin the storage device 130 in the form of instructions, and invokedand/or executed by the processing device 120 (e.g., the processor 210 ofthe computing device 200 as illustrated in FIG. 2, the GPU 330 or CPU340 of the mobile device 300 as illustrated in FIG. 3).

In 602, a plurality of groups of PET images and MR images of multiplesamples may be obtained. Operation 602 may be performed by theacquisition module 402 and/or the model determination module 406. Eachof the plurality of groups of PET images and MR images may include a PETimage and an MR image of a sample. A sample may include a subject asdescribed elsewhere in the present disclosure (e.g., FIG. 1 and thedescriptions thereof) or not. In some embodiments, the PET image (alsoreferred to as a second PET image) and the MR image (also referred to asa second MR image) may be acquired by a PET scanner and an MR scanner,respectively, such as a PET scanner and an MR scanner of amulti-modality imaging device (e.g., the medical device 110). Theacquisition module 402 may obtain the plurality of groups of PET imagesand MR images from the medical device 110, the storage device 130, thestorage 220, the storage 390, or any other storage device.

In 604, a plurality of reference attenuation correction images may beobtained. Operation 604 may be performed by the acquisition module 402and/or the model determination module 406. Each of the plurality ofreference attenuation correction images may correspond to one group ofthe plurality of groups of PET images and MR images of a sample. In someembodiments, a reference attenuation correction image (also referred toas third attenuation correction image) associated with a PET image andan MR image of a sample may be generated based on a CT image of thesample. In some embodiments, the CT image may be acquired by a CTscanner, such as a CT scanner of a multi-modality imaging device (e.g.,a PET-CT scanner) via scanning the sample. In some embodiments, the CTimage of a sample may be transformed into the reference attenuationcorrection image associated with a PET image of the sample using forexample, a scaling technique, an image segmentation technique, a Hybridtechnique, a bilinear technique, a dual-energy X-ray CT technique, etc.For example, using the scaling technique, the reference attenuationcorrection image associated with the PET image of the sample may bedetermined by multiplying a ratio and pixel values of the CT image. Asanother example, using the image segmentation technique, the samplepresented in the CT image may be identified and classified into variousportions (e.g., water, adipose tissues, bone tissues, lung, etc.). Thevarious portions may be designated with various attenuation coefficients(or values) according to clinical experience by a user or according to adefault setting of the imaging system 100. The reference attenuationcorrection image associated with the PET image may be determined basedon the various attenuation coefficients (or values) corresponding tovarious portions (e.g., water, adipose tissues, bone tissues, lungs,etc.) of the sample.

In some embodiments, a reference attenuation correction image associatedwith a PET image of a sample may be generated based on at least one ofthe PET image and the MR image. For example, the reference attenuationcorrection image associated with the PET image and the MR image may begenerated using a segmentation technique. Specifically, the samplepresented in the MR image and/or the PET image may be identified andclassified into different portions (e.g., water, adipose tissues, bonetissues, lungs, etc.). The various portions presented in the PET imageand the MR image may be fused and assigned various attenuationcoefficients (or values) according to clinical experience by a user oraccording to a default setting of the imaging system 100. The referenceattenuation correction image associated with the PET image and the MRimage may be determined based on the various attenuation coefficients(or values) corresponding to various portions (e.g., water, adiposetissue, bone tissue, lungs, etc.) of the subject. As another example,the attenuation correction image associated with the PET image and theMR image may be generated using a body map technique. Using the body maptechnique, the MR image may be registered with a reference body mapincluding various attenuation coefficients (or values) corresponding todifferent tissues or organs. The reference attenuation correction imageassociated with the PET image may be generated based on theregistration.

In 606, a neural network model may be obtained. Operation 606 may beperformed by the acquisition module 402 and/or the model determinationmodule 406. Exemplary neural network models may include a backpropagation (BP) neural network model, a radial basis function (RBF)neural network model, a deep belief nets (DBN) neural network model, anElman neural network model, or the like, or a combination thereof. Insome embodiments, the neural network model may include multiple layers,for example, an input layer, multiple hidden layers, and an outputlayer. The multiple hidden layers may include one or more convolutionallayers, one or more batch normalization layers, one or more activationlayers, a fully connected layer, a cost function layer, etc. Each of themultiple layers may include a plurality of nodes.

In some embodiments, the neural network model may be defined by aplurality of parameters. Exemplary parameters of the neural networkmodel may include the size of a convolutional kernel, the number oflayers, the number of nodes in each layer, a connected weight betweentwo connected nodes, a bias vector relating to a node, etc. Theconnected weight between two connected nodes may be configured torepresent a proportion of an output value of a node to be as an inputvalue of another connected node. The bias vector relating to a node maybe configured to control an output value of the node deviating from anorigin.

In 608, the neural network model may be trained using the plurality ofgroups of PET images and MR images, and the multiple attenuationcorrection data. Operation 608 may be performed by the modeldetermination module 406. Exemplary neural network training algorithmmay include a gradient descent algorithm, a Newton's algorithm, aQuasi-Newton algorithm, a Levenberg-Marquardt algorithm, a conjugategradient algorithm, or the like, or a combination thereof, asexemplified in FIG. 7 and the description thereof. In some embodiments,the neural network model may be trained by performing a plurality ofiterations. Before the plurality of iterations, the parameters of theneural network model may be initialized. For example, the connectedweights and/or the bias vector of nodes of the neural network model maybe initialized to be random values in a range, e.g., the range from −1to 1. As another example, all the connected weights of the neuralnetwork model may have a same value in the range from −1 to 1, forexample, 0. As still an example, the bias vector of nodes in the neuralnetwork model may be initialized to be random values in a range from 0to 1. In some embodiments, the parameters of the neural network modelmay be initialized based on a Gaussian random algorithm, a Xavieralgorithm, etc. Then the plurality of iterations may be performed toupdate the parameters of the neural network model until a condition issatisfied. The condition may provide an indication of whether the neuralnetwork model is sufficiently trained. For example, the condition may besatisfied if the value of a cost function associated with the neuralnetwork model is minimal or smaller than a threshold (e.g., a constant).As another example, the condition may be satisfied if the value of thecost function converges. The convergence may be deemed to have occurredif the variation of the values of the cost function in two or moreconsecutive iterations is smaller than a threshold (e.g., a constant).As still an example, the condition may be satisfied when a specifiednumber of iterations are performed in the training process.

For each of the plurality of iterations, a PET image and an MR image inone group of the plurality of groups of PET images and MR images and thecorresponding reference attenuation correction image may be inputtedinto the neural network model. The PET image and the MR image may beprocessed by one or more layers of the neural network model to generatean estimated attenuation correction image. The estimated attenuationcorrection image may be compared with the reference attenuationcorrection image associated with the PET image based on the costfunction of the neural network model. The cost function of the neuralnetwork model may be configured to assess a difference between a testingvalue (e.g., the estimated attenuation correction image) of the neuralnetwork model and a desired value (e.g., the reference attenuationcorrection image associated with the PET image). If the value of thecost function exceeds a threshold in a current iteration, the parametersof the neural network model may be adjusted and updated to cause thevalue of the cost function corresponding to the PET image and the MRimage (i.e., the difference between the estimated attenuation correctionimage and the reference attenuation correction image) smaller than thethreshold. Accordingly, in a next iteration, another group of a PETimage and an MR image, and a corresponding reference attenuationcorrection image may be inputted into the neural network model to trainthe neural network model as described above until the condition issatisfied.

In 610, a trained neural network model may be determined as a targetneural network model that provides a mapping relationship between PETimages, MR images, and corresponding attenuation correction images.Operation 610 may be performed by the model determination module 406.The trained neural network model may be configured to output anattenuation correction image associated with a specific PET image basedon the mapping relationship when the specific PET image and acorresponding MR image are inputted into the trained neural networkmodel. In some embodiments, the trained neural network model may bedetermined based on the updated parameters. In some embodiments, thetarget neural network model may be transmitted to the storage device130, the storage module 412, or any other storage device for storage.

In some embodiments, the target neural network model may be updatedbased on a testing performed on the target neural network model. If thetest result of the target neural network model does not satisfy acondition, the target neural network model may be updated. The targetneural network model may be tested based on one or more groups of testdata. A group of test samples may include a test PET image, a test MRimage, and a reference attenuation correction image associated with thetest PET image. A test PET image and a test MR image may be inputtedinto the target neural network model to output a predicted attenuationcorrection image associated with the test PET image. The predictedattenuation correction image associated with the test PET image may becompared with the reference attenuation correction image associated withthe test PET image. If the difference between the predicted attenuationcorrection image and the reference attenuation correction imageassociated with the test PET image is greater than a threshold, the testresult of the target neural network model is deemed not satisfying thecondition, and the target neural network model may need to be updated.The testing of the target neural network model may be performedaccording to an instruction of a user, clinical demands, or a defaultsetting of the imaging system 100. For example, the target neuralnetwork model may be tested at set intervals (e.g., every other month,every two months, etc.). As another example, the target neural networkmodel may be updated based on added data in a training set of the targetneural network model over a period of time. If the quantity of the addeddata in the training set over a period of time is greater than athreshold, the target neural network model may be updated based on theupdated training set. Accordingly, the target neural network model mayadapt to a complex clinical situation and have improved robustness.

It should be noted that the above description of the process ofallocating computing resources for medical applications in response torequests for performing the medical applications is merely provided forthe purposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. Such variations and modifications do not departfrom the scope of the present disclosure. For example, operation 602 andoperation 604 may be performed simultaneously. As another example,operation 610 may be omitted. In some embodiments, the convergence maybe deemed to have occurred if the variation of the values of the costfunction in two or more consecutive iterations is equal to a threshold(e.g., a constant).

FIG. 7 is a schematic diagram illustrating an exemplary convolutionalneural network (CNN) model according to some embodiments of the presentdisclosure.

The CNN model 700 may include an input layer 720, hidden layers 740, andan output layer 760. The multiple hidden layers 740 may include one ormore convolutional layers, one or more Rectified Linear Units layers(ReLU layers), one or more pooling layers, one or more fully connectedlayers, or the like, or a combination thereof.

For illustration purposes, exemplary hidden layers 740 of the CNN model,including a convolutional layer 740-1, a pooling layer 740-2, and afully connected layer 740-N, are illustrated. As described in connectionwith process 600, the model determination module 406 may acquire a PETimage, an MR image, and a reference attenuation correction imageassociated with the PET image as an input of the CNN model. The PETimage and the MR image may be denoted by matrixes including a pluralityof elements, respectively. A plurality of elements in a matrix may havea value (also referred to as pixel/voxel value) representing acharacteristic of the element. Values of at least one portion of theplurality of elements in the PET image and the MR image may be inputtedinto the hidden layers 740.

The convolutional layer 740-1 may include a plurality of kernels (e.g.,A, B, C, and D). The plurality of kernels may be used to extract a PETimage and an MR image of a subject. In some embodiments, each of theplurality of kernels may filter a portion (e.g., a region) of the PETimage and corresponding portion of the MR image of the subject toproduce a specific feature or area corresponding to the portion (e.g., aregion) of the PET image and corresponding portion of the MR image ofthe subject. The feature may include a low-level feature (e.g., an edgefeature, a texture feature), a high-level feature (e.g., a semanticfeature), or a complicated feature (e.g., a deep hierarchical feature)that is calculated based on the kernel(s).

The pooling layer 740-2 may take the output of the convolutional layer740-1 as an input. The pooling layer 740-2 may include a plurality ofpooling nodes (e.g., E, F, G, and H). The plurality of pooling nodes maybe used to sample the output of the convolutional layer 740-1, and thusmay reduce the computational load of data processing and increase thespeed of data processing of the imaging system 100. In some embodiments,the model determination module 406 may reduce the volume of the matrixcorresponding to a PET image and an MR image in the pooling layer 740-2.For example, the model determination module 406 may divide the PET imageand the MR image into multiple regions in the pooling layer 740-2. Theaverage of values of pixels in one of the multiple regions may bedesignated as the value of a pixel representing the one of the multipleregions.

The fully connected layer 740-N may include a plurality of neurons(e.g., O, P, M, and N). The plurality of neurons may be connected to aplurality of nodes from the previous layer, such as a pooling layer. Inthe fully connected layer 740-3, the model determination module 406 maydetermine a plurality of vectors corresponding to the plurality ofneurons based on the PET image and MR image and further weigh theplurality of vectors with a plurality of weighting coefficients.

The output layer 760 may determine an output, such as an attenuationcorrection image, based on the plurality of vectors and weightingcoefficients obtained in the fully connected layer 740-N. In someembodiments, the output layer 760 may specify the deviation ordifference between a predicted output (e.g., an estimated attenuationcorrection image associated with the PET image) and a true label (e.g.,a reference attenuation correction image associated with the PET image)based on a cost function. The deviation or difference between apredicted output (e.g., an estimated attenuation correction imageassociated with the PET image) and a true label (e.g., a referenceattenuation correction image associated with the PET image) may bedefined by a value of the cost function. If the value of the costfunction satisfies a condition, the training process of the CNN modelmay be completed. If the value of the cost function does not satisfy thecondition, the parameters of the CNN model may be updated using agradient descent algorithm. For example, if the predicted output (e.g.,a pixel value in an estimated attenuation correction image associatedwith the PET image) is less than the true label (e.g., a pixel value ina reference attenuation correction image associated with the PET image),one portion of the weightings of the CNN model may be increased. If thepredicted output (e.g., a pixel value in an estimated attenuationcorrection image associated with the PET image) exceeds the true label(e.g., a pixel value in a reference attenuation correction imageassociated with the PET image), one portion of the weightings of the CNNmodel may be decreased.

It shall be noted that the CNN model may be modified when applied indifferent conditions. For example, in a training process, a RectifiedLinear Units layer may be added. An activation function may be used bythe Rectified Linear Units layer to constrain an output of the RectifiedLinear Units layer. Exemplary activation functions may include a linearfunction, a ramp function, a threshold function, a Sigmoid function,etc.

In some embodiments, the model determination module 406 may get accessto multiple processing units, such as GPUs, in the imaging system 100.The multiple processing units may perform parallel processing in somelayers of the CNN model. The parallel processing may be performed insuch a manner that the calculations of different nodes in a layer of theCNN model may be assigned to two or more processing units. For example,one GPU may run the calculations corresponding to kernels A and B, andthe other GPU(s) may run the calculations corresponding to kernels C andD in the convolutional layer 740-1. Similarly, the calculationscorresponding to different nodes in another type of layers in the CNNmodel may be performed in parallel by the multiple GPUs.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “unit,” “module,” or “system.” Furthermore, aspects ofthe present disclosure may take the form of a computer program productembodied in one or more computer readable media having computer readableprogram code embodied thereon.

A non-transitory computer readable signal medium may include apropagated data signal with computer readable program code embodiedtherein, for example, in baseband or as part of a carrier wave. Such apropagated signal may take any of a variety of forms, includingelectro-magnetic, optical, or the like, or any suitable combinationthereof. A computer readable signal medium may be any computer readablemedium that is not a computer readable storage medium and that maycommunicate, propagate, or transport a program for use by or inconnection with an instruction execution system, apparatus, or device.Program code embodied on a computer readable signal medium may betransmitted using any appropriate medium, including wireless, wireline,optical fiber cable, RF, or the like, or any suitable combination of theforegoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution, e.g., an installationon an existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed subject matter requires more features thanare expressly recited in each claim. Rather, inventive embodiments liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities, properties, andso forth, used to describe and claim certain embodiments of theapplication are to be understood as being modified in some instances bythe term “about,” “approximate,” or “substantially.” For example,“about,” “approximate,” or “substantially” may indicate ±20% variationof the value it describes, unless otherwise stated. Accordingly, in someembodiments, the numerical parameters set forth in the writtendescription and attached claims are approximations that may varydepending upon the desired properties sought to be obtained by aparticular embodiment. In some embodiments, the numerical parametersshould be construed in light of the number of reported significantdigits and by applying ordinary rounding techniques. Notwithstandingthat the numerical ranges and parameters setting forth the broad scopeof some embodiments of the application are approximations, the numericalvalues set forth in the specific examples are reported as precisely aspracticable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

1. A system for attenuation correction comprising: at least one storagedevice storing executable instructions, and at least one processor incommunication with the at least one storage device, when executing theexecutable instructions, causing the system to: obtain at least onefirst PET image of a subject acquired by a PET scanner; obtain at leastone first MR image of the subject acquired by an MR scanner; obtain atarget neural network model that provides a mapping relationship betweenPET images, MR images, and corresponding attenuation correction data;generate, based on the at least one first PET image and the at least onefirst MR image of the subject, first attenuation correction datacorresponding to the subject using the target neural network model; anddetermine, based on the first attenuation correction data correspondingto the subject, a target PET image of the subject.
 2. The system ofclaim 1, wherein to obtain a target neural network model, the at leastone processor is further configured to cause the system to: obtainmultiple groups of training data, each of the multiple groups oftraining data including a second PET image, a second MR image, andsecond attenuation correction data corresponding to a sample; andgenerate the target neural network model by training a neural networkmodel using the multiple groups of training data.
 3. The system of claim2, wherein the neural network model includes at least one of aconvolutional neural network (CNN) model, a back propagation (BP) neuralnetwork model, a radial basis function (RBF) neural network model, adeep belief nets (DBN) neural network model, or an Elman neural networkmodel.
 4. The system of claim 2, wherein to obtain multiple groups oftraining data, the at least one processor is further configured to causethe system to: for each of the multiple groups of training data, obtaina CT image of the sample; and determine, the second attenuationcorrection data corresponding to the sample based on the CT image of thesample.
 5. The system of claim 2, wherein to obtain multiple groups oftraining data, the at least one processor is further configured to causethe system to: for each of the multiple groups of training data,determine, based on at least one of the second MR image or the secondPET image, the second attenuation correction data corresponding to thesample.
 6. The system of claim 1, wherein to generate, based on the atleast one first PET image and the at least one first MR image of thesubject, first attenuation correction data corresponding to the subjectusing the target neural network model, the at least one processor isfurther configured to cause the system to: input the at least one firstPET image and the at least one first MR image to the target neuralnetwork model; and obtain the first attenuation correction data outputby the target neural network model.
 7. The system of claim 1, wherein todetermine, based on the first attenuation correction data correspondingto the subject, a target PET image of the subject, the at least oneprocessor is further configured to cause the system to: obtain PETprojection data associated with the first PET image of the subject; andreconstruct, based on the PET projection data and the first attenuationcorrection data, the target PET image.
 8. The system of claim 7, whereinto determine, based on the first attenuation correction datacorresponding to the subject, a target PET image of the subject, the atleast one processor is further configured to cause the system to:perform a post-processing operation on the first attenuation correctiondata corresponding to the subject, the post-processing operationincluding at least one of an interpolation operation or a registrationoperation.
 9. The system of claim 1, wherein the at least one processoris further configured to cause the system to: perform a pre-processingoperation on at least one of the at least one first PET image or the atleast one first MR image, the pre-processing operation including atleast one of a filtering operation, a smoothing operation, atransformation operation, or a denoising operation.
 10. A method forattenuation correction implemented by a computing device, the computingdevice including at least one processor and at least one storage device,the method comprising: obtaining at least one first PET image of asubject acquired by a PET scanner; obtaining at least one first MR imageof the subject acquired by an MR scanner; obtaining a target neuralnetwork model that provides a mapping relationship between PET images,MR images, and corresponding attenuation correction data; generating,based on the at least one first PET image and the at least one first MRimage of the subject, first attenuation correction data corresponding tothe subject using the target neural network model; and determining,based on the first attenuation correction data corresponding to thesubject, a target PET image of the subject.
 11. The method of claim 10,wherein the obtaining a target neural network model includes: obtainingmultiple groups of training data, each of the multiple groups oftraining data including a second PET image, a second MR image, andsecond attenuation correction data corresponding to a sample; andgenerating the target neural network model by training a neural networkmodel using the multiple groups of training data.
 12. The method ofclaim 11, wherein the neural network model includes at least one of aconvolutional neural network (CNN) model, a back propagation (BP) neuralnetwork model, a radial basis function (RBF) neural network model, adeep belief nets (DBN) neural network model, or an Elman neural networkmodel.
 13. The method of claim 11, wherein the obtaining multiple groupsof training data includes: for each of the multiple groups of trainingdata, obtaining a CT image of the sample; and determining the secondattenuation correction data corresponding to the sample based on the CTimage of the sample.
 14. The method of claim 11, wherein the obtainingmultiple groups of training data includes: for each of the multiplegroups of training data, determining, based on the at least one of thesecond MR image or the second PET image, the second attenuationcorrection data corresponding to the sample.
 15. The method of claim 10,wherein the generating, based on the at least one first PET image andthe at least one first MR image of the subject, first attenuationcorrection data corresponding to the subject using the target neuralnetwork model includes: inputting the at least one first PET image andthe at least one first MR image to the target neural network model; andobtaining the first attenuation correction data output by the targetneural network model.
 16. The method of claim 10, wherein thedetermining, based on the first attenuation correction datacorresponding to the subject, a target PET image of the subjectincludes: obtaining PET projection data associated with the first PETimage of the subject; and reconstructing, based on the PET projectiondata and the first attenuation correction data, the target PET image.17. The method of claim 16, wherein the determining, based on the firstattenuation correction data corresponding to the subject, a target PETimage of the subject includes: performing a post-processing operation onthe first attenuation correction data corresponding to the subject, thepost-processing operation including at least one of an interpolationoperation or a registration operation.
 18. The method of claim 10,further comprising: performing a pre-processing operation on at leastone of the at least one first PET image or the at least one first MRimage, the pre-processing operation including at least one of afiltering operation, a smoothing operation, a transformation operation,or a denoising operation.
 19. A non-transitory computer-readable mediumstoring at least one set of instructions, wherein when executed by atleast one processor, the at least one set of instructions directs the atleast one processor to perform acts of: obtaining at least one first PETimage of a subject acquired by a PET scanner; obtaining at least onefirst MR image of the subject acquired by an MR scanner; obtaining atarget neural network model that provides a mapping relationship betweenPET images, MR images, and corresponding attenuation correction data;generating, based on the at least one first PET image and the at leastone first MR image of the subject, first attenuation correction datacorresponding to the subject using the target neural network model; anddetermining, based on the first attenuation correction datacorresponding to the subject, a target PET image of the subject. 20.(canceled)